A Phishing Detection System based on Machine Learning
Cheyu Wu, Cheng-Chung Kuo, Chu‐Sing Yang · 2019
As the Internet has become an essential part of human beings' lives, a growing number of people are enjoying the convenience brought by the Internet, while more are attacks coming from on the dark side of the Internet. Based on some weaknesses of human nature, hackers have designed confusing phishing pages to entice web viewers to proactively expose their privacy, sensitive information.In this article, we propose a URL-based detection system - combining the URL of the web page URL and the URL of the web page source code as features, import Levenshtein Distance as the algorithm for calculating the similarity of strings, and supplemented by the machine learning architecture. Due to the great accuracy in small sample numbers and binary classification, we implement Support-vector machine to be the machine learning algorithm model in our system. The system is designed to provide high accuracy and low false positive rate detection results for unknown phishing pages.